class_docstr: Feature extractor for images using a regular CNN.
  
  By using the notion of an "endpoint", we truncate a classification CNN at
  a certain layer output, and return this partial feature map to be used as
  a good image representation for other ML tasks.
docstr: Args:
      inputs: A Tensor of shape `(batch_size, height, width, channels)`.
  
  Returns:
      feature_map: A Tensor of shape
          `(batch_size, feature_map_height, feature_map_width, depth)`.
          The resulting dimensions depend on the CNN architecture, the
          endpoint used, and the dimensions of the input images.
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code: def _build(self, inputs, is_training=False):
      """
      Args:
          inputs: A Tensor of shape `(batch_size, height, width, channels)`.
  
      Returns:
          feature_map: A Tensor of shape
              `(batch_size, feature_map_height, feature_map_width, depth)`.
              The resulting dimensions depend on the CNN architecture, the
              endpoint used, and the dimensions of the input images.
      """
      pred = super(TruncatedBaseNetwork, self)._build(
          inputs, is_training=is_training
      )
  
      return self._get_endpoint(dict(pred["end_points"]))
  
file_path: luminoth/luminoth/models/base/truncated_base_network.py
imports: import tensorflow
  import tensorflow.contrib.slim
  from tensorflow.contrib.slim.nets import resnet_utils, resnet_v1
  from luminoth.models.base import BaseNetwork
label_desc: Use image node_id 2 for calling the TruncatedBaseNetwork obj's underlying member method code with example usage: obj._build(inputs, is_training) and returns: self